Future work in the area can include improved analysis of the data in the under performing models. For example, in Elastic-Net model Bayesian information criterion (BIC) [1]} can be used for tuning feature selection. For variable selection, we can also consider the minimax concave penalty (MCP)[2]} and the Smoothly Clipped Absolute Deviation (SCAD) penalty [3]}, since they both possess better theoretical property than the Lasso penalty. Unsupervised learning can also be used to draw connections between feature groupings, and modeling features to discover key characteristics. Parameter tuning for models can also be explored to develop even more accurate predictive models.
